docstr: Apply a 1D adaptive average pooling over an input signal composed of
  several input planes.
  
  Parameters
  ----------
  self
      Input array. Must have shape (N, C, L_in) or (C, L_in) where N is
      the batch dimension, C is the feature dimension, and L_in is the spatial
      dimension.
  output_size
      Spatial output size.
  
  Returns
  -------
      The result of the pooling operation. Will have shape (N, C, L_out) or
      (C, L_out), where L_out = `output_size`
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code: def adaptive_avg_pool1d(
      self: ivy.Array,
      output_size: int,
  ) -> ivy.Array:
      """Apply a 1D adaptive average pooling over an input signal composed of
      several input planes.
  
      Parameters
      ----------
      self
          Input array. Must have shape (N, C, L_in) or (C, L_in) where N is
          the batch dimension, C is the feature dimension, and L_in is the spatial
          dimension.
      output_size
          Spatial output size.
  
      Returns
      -------
          The result of the pooling operation. Will have shape (N, C, L_out) or
          (C, L_out), where L_out = `output_size`
      """
      return ivy.adaptive_avg_pool1d(
          self._data,
          output_size,
      )
  
imports: import abc
  from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import ivy
label_desc: Use image node_id 14 for calling the _ArrayWithLayersExperimental obj's underlying member method code with example usage: obj.adaptive_avg_pool1d(output_size) and returns: ivy